Kinney, J. B. (July 2014) Estimation of probability densities using scale-free field theories. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 90 (1). Article no. 011301. ISSN 15502376
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Abstract
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics. Previous work has argued that this problem can be addressed in a natural way using methods from statistical field theory. Here I describe results that allow this field-theoretic approach to be rapidly and deterministically computed in low dimensions, making it practical for use in day-to-day data analysis. Importantly, this approach does not impose a privileged length scale for smoothness of the inferred probability density, but rather learns a natural length scale from the data due to the tradeoff between goodness of fit and an Occam factor. Open source software implementing this method in one and two dimensions is provided. © Published by the American Physical Society.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > quantitative biology |
| CSHL Authors: | |
| Communities: | CSHL labs > Kinney lab CSHL Cancer Center Program > Gene Regulation and Cell Proliferation |
| Depositing User: | Matt Covey |
| Date: | 11 July 2014 |
| Date Deposited: | 01 Aug 2014 15:51 |
| Last Modified: | 13 Oct 2015 14:49 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/30662 |
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